Take home Exercise 3

1. Overview

Through visual analytics, FishEye aims to identify companies potentially engaged in illegal fishing and protect marine species affected by it.

In this context, this page will attempt to answer the following task under Mini-Challenge 3 of the VAST Challenge: Use visual analytics to identify anomalies in the business groups present in the knowledge graph. Limit your response to 400 words and 5 images.

1.1 Import Libraries and Datasets

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pacman::p_load(jsonlite, tidygraph, ggraph, 
               visNetwork, graphlayouts, ggforce, 
               skimr, tidytext, tidyverse, ggplot2)

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mc3_data <- fromJSON("data/MC3.json")

1.2 Extract Edges

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mc3_edges <- as_tibble(mc3_data$links) %>% 
  distinct() %>%
  mutate(source = as.character(source),
         target = as.character(target),
         type = as.character(type)) %>%
  group_by(source, target, type) %>%
    summarise(weights = n()) %>%
  filter(source!=target) %>%
  ungroup()

1.2.1 Check missing value in Edges

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colSums(is.na(mc3_edges))

 source  target    type weights 
      0       0       0       0 

1.2.2 Check duplicates in Edges

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mc3_edges[duplicated(mc3_edges),]

# A tibble: 0 × 4
# ℹ 4 variables: source <chr>, target <chr>, type <chr>, weights <int>

1.3 Extract & View Nodes

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mc3_nodes <- as_tibble(mc3_data$nodes) %>%
  mutate(country = as.character(country),
         id = as.character(id),
         product_services = as.character(product_services),
         revenue_omu = as.numeric(as.character(revenue_omu)),
         type = as.character(type)) %>%
  select(id, country, type, revenue_omu, product_services)

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mc3_nodes

# A tibble: 27,622 × 5
   id                                 country type  revenue_omu product_services
   <chr>                              <chr>   <chr>       <dbl> <chr>           
 1 Jones LLC                          ZH      Comp…  310612303. Automobiles     
 2 Coleman, Hall and Lopez            ZH      Comp…  162734684. Passenger cars,…
 3 Aqua Advancements Sashimi SE Expr… Oceanus Comp…  115004667. Holding firm wh…
 4 Makumba Ltd. Liability Co          Utopor… Comp…   90986413. Car service, ca…
 5 Taylor, Taylor and Farrell         ZH      Comp…   81466667. Fully electric …
 6 Harmon, Edwards and Bates          ZH      Comp…   75070435. Discount superm…
 7 Punjab s Marine conservation       Riodel… Comp…   72167572. Beef, pork, chi…
 8 Assam   Limited Liability Company  Utopor… Comp…   72162317. Power and Gas s…
 9 Ianira Starfish Sagl Import        Rio Is… Comp…   68832979. Light commercia…
10 Moran, Lewis and Jimenez           ZH      Comp…   65592906. Automobiles, tr…
# ℹ 27,612 more rows

1.3.1 Check Missing value in Nodes

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colSums(is.na(mc3_nodes))

              id          country             type      revenue_omu 
               0                0                0            21515 
product_services 
               0 

21515 missing from revenue_omu column

1.3.2 Check Duplicates in Nodes

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mc3_nodes[duplicated(mc3_nodes),]

# A tibble: 2,595 × 5
   id                              country type    revenue_omu product_services
   <chr>                           <chr>   <chr>         <dbl> <chr>           
 1 Smith Ltd                       ZH      Company          NA Unknown         
 2 Williams LLC                    ZH      Company          NA Unknown         
 3 Garcia Inc                      ZH      Company          NA Unknown         
 4 Walker and Sons                 ZH      Company          NA Unknown         
 5 Walker and Sons                 ZH      Company          NA Unknown         
 6 Smith LLC                       ZH      Company          NA Unknown         
 7 Smith Ltd                       ZH      Company          NA Unknown         
 8 Romero Inc                      ZH      Company          NA Unknown         
 9 Niger River   Marine life       Oceanus Company          NA Unknown         
10 Coastal Crusaders AS Industrial Oceanus Company          NA Unknown         
# ℹ 2,585 more rows

Note

There are 2595 dupe entries

1.3.3 remove dupliated rows in Nodes.

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mc3_nodes_unique <- distinct(mc3_nodes)

mc3_nodes_unique

# A tibble: 25,027 × 5
   id                                 country type  revenue_omu product_services
   <chr>                              <chr>   <chr>       <dbl> <chr>           
 1 Jones LLC                          ZH      Comp…  310612303. Automobiles     
 2 Coleman, Hall and Lopez            ZH      Comp…  162734684. Passenger cars,…
 3 Aqua Advancements Sashimi SE Expr… Oceanus Comp…  115004667. Holding firm wh…
 4 Makumba Ltd. Liability Co          Utopor… Comp…   90986413. Car service, ca…
 5 Taylor, Taylor and Farrell         ZH      Comp…   81466667. Fully electric …
 6 Harmon, Edwards and Bates          ZH      Comp…   75070435. Discount superm…
 7 Punjab s Marine conservation       Riodel… Comp…   72167572. Beef, pork, chi…
 8 Assam   Limited Liability Company  Utopor… Comp…   72162317. Power and Gas s…
 9 Ianira Starfish Sagl Import        Rio Is… Comp…   68832979. Light commercia…
10 Moran, Lewis and Jimenez           ZH      Comp…   65592906. Automobiles, tr…
# ℹ 25,017 more rows

1.4 Explore Edge data structure

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skim(mc3_edges)

Data summary

Name	mc3_edges
Number of rows	24036
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	6	700	0	12856	0
target	0	1	6	28	0	21265	0
type	0	1	16	16	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
weights	0	1	1	0	1	1	1	1	1	▁▁▇▁▁

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DT::datatable(mc3_edges)

1.4.1 Edges by Type

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ggplot(data = mc3_edges,
       aes(x = type)) +
  geom_bar()

1.5 Initial Network Graph Analysis (Kick-starter)

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id1 <- mc3_edges %>%
  select(source) %>%
  rename(id = source)
id2 <- mc3_edges %>%
  select(target) %>%
  rename(id = target)
mc3_nodes1 <- rbind(id1, id2) %>%
  distinct() %>%
  left_join(mc3_nodes,
            unmatched = "drop")

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mc3_graph <- tbl_graph(nodes = mc3_nodes1,
                       edges = mc3_edges,
                       directed = FALSE) %>%
  mutate(betweenness_centrality = centrality_betweenness(),
         closeness_centrality = centrality_closeness())

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mc3_graph %>%
  filter(betweenness_centrality >= 100000) %>%
ggraph(layout = "fr") +
  geom_edge_link(aes(alpha=0.5)) +
  geom_node_point(aes(
    linewidth = betweenness_centrality,
    colors = "lightblue",
    alpha = 0.5)) +
  scale_linewidth_continuous(range=c(1,10))+
  theme_graph() +
  theme(text = element_text(family = "sans"))

1.4 Explore Node data structure

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top_products <- mc3_nodes_unique %>%
  count(product_services, sort = TRUE) %>%
  top_n(10)

# Rename the columns
top_products <- top_products %>%
  rename(Products = product_services, Occurrences = n)

# Print the table
print(top_products)

# A tibble: 10 × 2
   Products                                                          Occurrences
   <chr>                                                                   <int>
 1 character(0)                                                            16395
 2 Unknown                                                                  4614
 3 Fish and seafood products                                                  63
 4 Seafood products                                                           55
 5 Fish and fish products                                                     31
 6 Food products                                                              31
 7 Canning, processing and manufacturing of seafood and other aquat…          23
 8 Footwear                                                                   21
 9 Seafood                                                                    20
10 Grocery products                                                           19

Note

charactor(0) and Unknown will be cleaned later.

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skim(mc3_nodes1)

Data summary

Name	mc3_nodes1
Number of rows	37324
Number of columns	5
_______________________
Column type frequency:
character	4
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
id	0	1.00	6	700	0	34121	0
country	29241	0.22	2	14	0	78	0
type	29241	0.22	7	16	0	3	0
product_services	29241	0.22	4	1737	0	1844	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
revenue_omu	34139	0.09	939014	12435469	3652.23	8261.03	16966.67	48266.67	310612303	▇▁▁▁▁

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DT::datatable(mc3_nodes1)

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ggplot(data = mc3_nodes1,
       aes(x = type)) +
  geom_bar()

2 Text Analytics with tidytext

In this section, I will perform basic text sensing using appropriate functions of tidytext package.

2.1 Record those “Unknwon” or”Charactor(0)” to “NA”

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mc3_nodes1$product_services[mc3_nodes1$product_services == "Unknown"] <- NA
mc3_nodes1$product_services[mc3_nodes1$product_services == "character(0)"] <- NA

2.2 Tokenisation

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token_nodes <- mc3_nodes1 %>%
  unnest_tokens(word, 
                product_services,
                to_lower = TRUE,
    # Exclude punctuation from tokenisation result
                strip_punct = TRUE)

2.3 Visualize the words extracted

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token_nodes %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")

2.4 Remove stopwords

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stopwords_removed <- token_nodes %>% 
  anti_join(stop_words)

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stopwords_removed %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")

2.5 remove the rows with stop words + “NA”

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stopwords_removed <- token_nodes %>% 
  anti_join(stop_words) %>%
  filter(!is.na(word)) 

stopwords_removed %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  geom_text(aes(label = n), vjust = 0.5, hjust = -0.1, size = 2, color = "black")+
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of Top 15 unique words found in product_services field")+
  theme_minimal() 

2.5.1 Count of words after removing stopwords and NA

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stopwords_removed_unique <- stopwords_removed %>% 
  filter(!is.na(word))

length(unique(stopwords_removed_unique$word))

[1] 5467

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length(unique(token_nodes$word))

[1] 5688

Note

Looks much neater now, about ~200 unique removed as part of stopwords removal. However, there are still with a lot of words not related to fishery

2.6 Filter by those words only related to fishery

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words_fishery <- c("fish", "seafood", "frozen", "food", "fresh", "salmon", "shrimp", "shellfish", "sea", "squid", "water", "seafoods", "foods", "marine", "shipment", "shipping", "pier", "carp", "cod", "herring", "lichen", "mackerel", "pollock", "shark", "tuna", "ocean", "oyster", "clam", "lobster", "crab", "crustaceans", "crustacean", "bass")

mc3_nodes_fishery <- mc3_nodes_unique %>%
  filter(str_detect(product_services, paste(words_fishery, collapse = "|", sep = "")) | is.na(product_services))

print(mc3_nodes_fishery)

# A tibble: 1,534 × 5
   id                                 country type  revenue_omu product_services
   <chr>                              <chr>   <chr>       <dbl> <chr>           
 1 Makumba Ltd. Liability Co          Utopor… Comp…   90986413. Car service, ca…
 2 Harmon, Edwards and Bates          ZH      Comp…   75070435. Discount superm…
 3 Punjab s Marine conservation       Riodel… Comp…   72167572. Beef, pork, chi…
 4 Fisher Group                       ZH      Comp…   29981457. Steel (marketin…
 5 Morales, Young and Taylor          ZH      Comp…   23739782. Processed foods…
 6 Morgan LLC                         ZH      Comp…   17939781. Animal feed, an…
 7 Neptune's Harvest LC Transport     Riodel… Comp…    8726579. Frozen whole an…
 8 Victoria Falls   Limited Liabilit… Rio Is… Comp…    8014806. Domestic and in…
 9 Caracola del Mar NV Family         Rio Is… Comp…    7085566. Canned, frozen …
10 The Sea Lion NV Marine biology     Oceanus Comp…    6264744. One- to five-da…
# ℹ 1,524 more rows

2.7 Match ID from Nodes to Source in Edge

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targets <- mc3_edges %>%
  filter(source %in% mc3_nodes_fishery$id) %>%
  select(target)

2.7.1 Filter mc_edges with the extracted targets

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mc3_edges_new <- mc3_edges %>%
  filter(target %in% targets$target)

2.7.2 Define “Non_targets” a targets that are not selected from mc3_edges

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Non_targets <- mc3_edges %>%
  filter(!target %in% targets$target) %>%
  distinct(target, .keep_all = TRUE)

2.7.3 Remove non_targets from Node_fishery

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mc3_nodes_fishery_new <- mc3_nodes_fishery %>%
  filter(!mc3_nodes_fishery$id %in% Non_targets$source)

Note

Result: none of the non-targets are in Node_fishery

2.8 Additional Data Clean Ups

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skim(mc3_edges_new)

Data summary

Name	mc3_edges_new
Number of rows	3711
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	7	213	0	1493	0
target	0	1	6	27	0	2887	0
type	0	1	16	16	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
weights	0	1	1	0	1	1	1	1	1	▁▁▇▁▁

Note

Maximum length of source is whopping 213, this is likely an input with lot of c(“,”) values.

2.8.1 Clean up Source Values

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mc3_edges_new_filtered <- mc3_edges_new %>%
  filter(startsWith(source, "c("))

#step 2

mc3_edges_new_split <- mc3_edges_new_filtered %>%
  separate_rows(source, sep = ", ") %>%
  mutate(source = gsub('^c\\(|"|\\)$', '', source))

#remove rows with grouped

mc3_edges_new2 <- mc3_edges_new %>%
  anti_join(mc3_edges_new_filtered)
        
#Add rows in step #2

mc3_edges_new2 <- mc3_edges_new2 %>%
  bind_rows(mc3_edges_new, mc3_edges_new_split)

#group

mc3_edges_new_groupby <- mc3_edges_new2 %>%
  group_by(source, target, type) %>%
  summarize(weight = n()) %>%
  filter(weight >1) %>%
  ungroup()

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#The maximum length of source looks more reasonable now at 57, suggesting all the multiple values have been dropped. 
skim(mc3_edges_new_groupby)

Data summary

Name	mc3_edges_new_groupby
Number of rows	3703
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
source	0	1	7	57	0	1485	0
target	0	1	6	27	0	2887	0
type	0	1	16	16	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
weight	0	1	2	0.09	2	2	2	2	5	▇▁▁▁▁

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class(mc3_edges_new_groupby)

[1] "tbl_df"     "tbl"        "data.frame"

Note

Maximum length of source has reduced from 213 to 57, after removing lot of c(“,”) values.

2.9 Link the latest edges to the nodes

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source_missing <-setdiff(mc3_edges_new_groupby$source, mc3_nodes_fishery_new$id)

source_missing_df <- tibble(
  id = source_missing,
  country = rep(NA_character_, length(source_missing)),
  type = rep("Company", length(source_missing)),
  revenue = rep(NA_real_, length(source_missing)),
  product_services = rep(NA_character_, length(source_missing))
)

target_missing <- setdiff(mc3_edges_new_groupby$target, mc3_nodes_fishery_new$id)

target_missing_df <- tibble(
  id = target_missing,
  country = rep(NA_character_, length(target_missing)),
  type = rep("Company", length(target_missing)),
  revenue = rep(NA_real_, length(target_missing)),
  product_services = rep(NA_character_, length(target_missing))
)

2.10 Keep only id values from Nodes df also present in the edges df

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mc3_nodes_fishery_new_filtered <- mc3_nodes_fishery_new %>%
  filter(id %in% c(mc3_edges_new_groupby$source, mc3_edges_new_groupby$target))

mc3_nodes_fishery_new_df <- bind_rows(mc3_nodes_fishery_new_filtered, source_missing_df, target_missing_df)

mc3_nodes_fishery_new_df <- mc3_nodes_fishery_new_df %>%
  mutate(revenue_omu = as.character(revenue_omu))

2.11 Cleaned Nodes Dataframe grouped by Id

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mc3_nodes_fishery_grouped <- mc3_nodes_fishery_new_df %>%
  group_by(id) %>%
  summarize(
    count = n(),
    type_1 = ifelse(n() >= 1, type[1], NA),
    type_2 = ifelse(n() >= 2, type[2], NA),
    type_3 = ifelse(n() >= 3, type[3], NA),
    country = ifelse(n() == 1, country, paste(unique(country), collapse = ", ")),
    revenue_omu = ifelse(n() == 1, revenue_omu, paste(unique(revenue_omu), collapse = ", ")),
    product_services = ifelse(n() == 1, product_services, paste(unique(product_services), collapse = ", "))
  )

2.11.1 Check Top 10 products of the Cleaned up Nodes Dataframe

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top_products <- mc3_nodes_fishery_grouped %>%
  count(product_services, sort = TRUE) %>%
  top_n(10)

# Rename the columns
top_products <- top_products %>%
  rename(Products = product_services, Occurrences = n)

# Print the table
print(top_products)

# A tibble: 10 × 2
   Products                                                          Occurrences
   <chr>                                                                   <int>
 1 <NA>                                                                     3560
 2 Fish and seafood products                                                  37
 3 Seafood products                                                           23
 4 Canning, processing and manufacturing of seafood and other aquat…          18
 5 Fish and fish products                                                     15
 6 Seafood                                                                    11
 7 Fish and sea food products                                                  9
 8 Fish and seafoods products                                                  9
 9 Fresh and frozen seafood                                                    9
10 Tuna, sword fish, bass, trout, and salmon, as well as offers she…           9

2.12 Define Fishery Edges and Nodes by selecting only those IDs that are related to Product_services in Words_Fishery defined earlier

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ids_fishing <- mc3_nodes_fishery_grouped %>%
  filter(str_detect(product_services, paste(words_fishery, collapse = "|", sep = "")) | is.na(product_services)) %>%
  pull(id)

mc3_edges_fishery <- mc3_edges_new_groupby %>%
  filter(source %in% ids_fishing)

mc3_nodes_fishery_ <- mc3_nodes_fishery_grouped %>%
  filter(id %in% c(mc3_edges_fishery$source, mc3_edges_fishery$target))

3 Network Analysis with Refined Graph with the Fishery edges and nodes

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mc3_fish_graph <- tbl_graph(nodes = mc3_nodes_fishery_,
                       edges = mc3_edges_fishery,
                       directed = FALSE) %>%
  mutate(betweenness_centrality = centrality_betweenness()) %>%
  ggraph(layout = "nicely") +
  scale_edge_width(range = c(0.01, 6)) +
  geom_node_point(aes(colour = type_1,
                      size = betweenness_centrality)) +
  theme_graph() +
  labs(size = "Betweenness Centrality")

mc3_fish_graph 

Note

I can observe that almost all of the refined Nodes has type equal to “Company”, with a few nodes with relatively high betweenness centrality near the center

3.1 Plot an Interative Network graph

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mc3_edges_fishery_in <- mc3_edges_fishery %>%
  rename(from = source, to = target)

mc3_nodes_fishery_in <- mc3_nodes_fishery_ %>%
  rename(group = type_1) 

# Create a visNetwork object with nodes and edges
visNetwork(nodes = mc3_nodes_fishery_in, edges = mc3_edges_fishery_in) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE) %>%
  visLegend() %>%
  visLayout(randomSeed = 123)

3.2 Find Targets () linked to multiple companies, which may be indications of anomalies.

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mc3_edges_fishery %>%
  group_by(target) %>%
  filter(n_distinct(source) > 1) %>%
  select(target) 

# A tibble: 1,324 × 1
# Groups:   target [508]
   target           
   <chr>            
 1 Elizabeth Jones  
 2 Michael Morrison 
 3 Amanda Robinson  
 4 Andrew Taylor    
 5 Brandon Cruz     
 6 Michael Thompson 
 7 Melissa Martin   
 8 Christopher Ramos
 9 Richard Smith    
10 Andrew Reed      
# ℹ 1,314 more rows

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mc3_edges_fishery %>%
  group_by(target) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  select(target, count)

# A tibble: 2,887 × 2
   target           count
   <chr>            <int>
 1 Michael Johnson     11
 2 John Smith          10
 3 Brian Smith          8
 4 Jennifer Johnson     8
 5 Michael Smith        8
 6 Richard Smith        8
 7 David Smith          7
 8 James Brown          7
 9 James Smith          7
10 Melissa Brown        7
# ℹ 2,877 more rows

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#write.csv(mc3_edges_fishery, file='C:/Users/junso/Downloads/mc3_edges_fishery.csv')

3.2.1 Distribution of Targets linked to multiple companies

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filtered_data <- mc3_edges_fishery %>%
  group_by(target) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  select(target, count)

filtered_data %>% 
  group_by(count) %>% 
  summarise(n = n()) %>% 
  mutate(percentage = round(n/sum(n) * 100, 2)) %>% 
  ggplot(aes(x = count, y = n, label = paste0(round(percentage, 2), "%"))) + 
  geom_bar(stat = "identity", fill = "lightblue", color = "black") + 
  xlab("Count") + 
  ylab("n") + 
  scale_x_continuous(breaks = unique(filtered_data$count)) + 
  geom_text(position = position_stack(vjust = 0.5))

Note

Nearly 80% of the targets have count of 1. Decided to use the Pareto rules to look at the top 20% only.

3.2.2 Distribution of Targets linked to at least 2 companies

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filtered_data2 <- mc3_edges_fishery %>%
  group_by(target) %>%
  summarise(count = n()) %>%
  filter(count > 1) %>%
  arrange(desc(count)) %>%
  select(target, count)

filtered_data2 %>%
  group_by(count) %>% 
  summarise(n = n()) %>% 
  mutate(percentage = round(n/sum(n) * 100, 2)) %>% 
  ggplot(aes(x = count, y = n, label = paste0(round(percentage, 2), "%"))) + 
  geom_bar(stat = "identity", fill = "lightblue", color = "black") + 
  xlab("Count") + 
  ylab("n") + 
  scale_x_continuous(breaks = unique(filtered_data$count)) + 
  geom_text(position = position_stack(vjust = 0.5))

Note

Left with about 500 companies. The future work is that there should be a way to define the cut-off value on company ownership for creating a subgraph that focuses on company owners who own relatively higher number of comapnies. Concious of time, I have decided to look at just top 5 targets in terms of the number of counts, starting with Michael Johnson.

3.2.3 Network Analysis on Michael Johnson

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Michael_Johnson <- unique(mc3_edges_fishery_in$from[mc3_edges_fishery_in$to == "Michael Johnson"])

Michael_Johnson_edges <- subset(mc3_edges_fishery_in, from %in% Michael_Johnson)

Michael_Johnson_nodes <- subset(mc3_nodes_fishery_, id %in% c(Michael_Johnson_edges$from, Michael_Johnson_edges$to))

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# Create a visNetwork
visNetwork(nodes = Michael_Johnson_nodes, edges = Michael_Johnson_edges) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE) %>%
  visLegend() %>%
  visLayout(randomSeed = 123)

Note

Michael Johnson is a sole owner of many smaller entities. Revenue of most of the companies are unknown, hinting us some of these might be paper companies

3.2.4 Network Analysis on Top 5 Targets

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top5 <- mc3_edges_fishery_in$from[mc3_edges_fishery_in$to %in% c("Michael Johnson", "John Smith", "Brian Smith", "Jennifer Johnson", "Michael Smith")]

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top5_edges <- subset(mc3_edges_fishery_in, from %in% top5)

top5_nodes <- subset(mc3_nodes_fishery_, id %in% c(top5_edges$from, top5_edges$to))

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# Create a visNetwork
visNetwork(nodes = top5_nodes, edges = top5_edges) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE) %>%
  visLegend() %>%
  visLayout(randomSeed = 123)

Note

The analysis on Top 5 targets verifies that Michael Johnson is not a unique case. Top 5 targets are sole owners of many smaller entities. Revenue of most of the companies they own are unknown, hinting us some of these might be paper companies possibly involved in transshipment.

4 Conclusion

Through data exploration, I was able to observe a few anomalies.

1. Individuals who have ownership in multiple companies. From analyzing three sub-network graphs, it was observed that these individuals tend to own a combination of large and small firms from various countries. While there is a possibility that everything is legitimate, it would be beneficial for FishEye to conduct a thorough examination of these individuals who own companies across borders, particularly when they are the sole owners of smaller entities, as exemplified in the case of ‘Michael Johnson’ and other top targets. Revenue of most of the companies they own are unknown from the data given, which may require authorities and regulatory bodies’ scrutiny

2. Almost all of the refined Nodes has type equal to “Company”, there were no beneficial owners and company contacts. This is expected looking from the distribution of types for Node Dataframe intiially, but it might be noteworthy to investigate further what id type mean for illegal fishery.